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The change of canopy statistical temperature eigenvalue is one of the important index for crop pest identification. However, with the effects of environmental temperature and humidity fluctuations, when canopy temperature is used directly in the time series for pest evaluation, the healthy plants must be set for comparison. Therefore, the method is not operable in practical production applications. In order to find an effective method for evaluating the canopy statistical temperature eigenvalues of rice plants after brown planthopper infestation, the brown planthopper susceptible rice variety “TN1” was taken as the object, and two treatments of brown planthopper infestation and non-infestation were set. The infrared canopy was used to obtain the canopy of rice. The temperature eigenvalues were evaluated by using a machine learning classifier to evaluate the temperature characteristics of rice canopy-induced thermal images of rice canopy. In data analysis, three canopy statistical temperature eigenvalues extracted from the thermal images were used, and the features that best reflected the differences were selected. The cumulative difference of the canopy temperature coefficient of variation was 30.78. And then, combined with air temperature, relative humidity and water temperature, the logistic regression and support vector machine were used to fit the evaluation model. For determining brown rice planthopper damage by the logistic regression algorithm，when three canopy statistical temperature eigenvalues were used as input vector, the accuracy of the logistic regression test set was 87.15%, the recall rate was 86.54%, and the F1-measure was 86.55%. Support vector machine algorithm test set accuracy rate was 86.74%, recall rate was 86.90%, and F1-measure was 86.53%. In practical applications, the statistical eigenvalue of the canopy thermal image of rice can be obtained by calculating the air temperature, relative humidity and water temperature information to evaluate whether the inversion showed the invasion of brown rice planthopper. It was of great significance for the health monitoring and diagnosis of rice.